At‐home resistance tubing strength training increases shoulder strength in the trained and untrained limb
Bibliographic record
Abstract
The purpose was to determine if an at-home resistance tubing strength training program on one shoulder (that is commonly used in rehabilitation settings) would produce increases in strength in the trained and untrained shoulders via cross-education. Twenty-three participants were randomized to TRAIN (strength-trained one shoulder; n = 13) or CONTROL (no intervention; n = 10). Strength training was completed at home using resistance tubing and consisted of maximal shoulder external rotation, internal rotation, scaption, retraction, and flexion 3 days/week for 4 weeks. Strength was measured via handheld dynamometry and muscle size measured via ultrasound. For external rotation strength, the trained (10.9 ± 10.9%) and untrained (12.7 ± 9.6%) arm of TRAIN was significantly different than CONTROL (1.6 ± 13.2%; -2.7 ± 12.3%; pooled across arm; P < 0.05). For internal rotation strength, the trained (14.8 ± 11.3%) and untrained (14.6 ± 10.1%) arm of TRAIN was significantly different than CONTROL (6.4 ± 11.2%; 5.1 ± 8.8%; pooled across arm; P < 0.05). There were no significant differences for scaption strength (P = 0.056). TRAIN significantly increased muscle size in the training arm of the supraspinatus (1.90 ± 0.32 to 1.99 ± 0.31 cm), and the anterior deltoid (1.08 ± 0.37 to 1.21 ± 0.39 cm; P < 0.05). This study suggests that an at-home resistance tubing training program on one limb can produce increases in strength in both limbs, and has implications for rehabilitation after unilateral shoulder injuries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".